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Projekt Fear!

  #10 (permalink)

Reno, Nevada
 
Trading Experience: Advanced
Platform: NinjaTrader
Favorite Futures: ZN, ZB, CL
 
phantomtrader's Avatar
 
Posts: 224 since May 2011
Thanks: 48 given, 356 received

Reality Checks


This is actually a great topic which deserves more in depth analysis. As traders, it's important to understand the realities of our universe. As Peter said in so many words, it's a waste of time to worry about HFT's and "stop hunters" because we are not privy to that information. And even if we had that information, the opportunity to capitalize on it would be out of reach for most of us. Worst of all is using these phenomena as an excuse for bad trades - creating fantasies doesn't provide a solution to why we make bad trades. The market gives us only the high, low, open, close. Peter gave us Jigsaw and others have given us footprint charts. The Jigsaw DOM is a phenomenal development - there are so many ways to use it.

There was an article last year in Research in International Business and Finance which describes the current status of high frequency trading and its impact on the market. I'm posting the Abstract, part of the Introduction and Conclusion as well as a few jpgs from the illustrations. If anyone wants the full article, please pm me and I'll send you the pdf - it's only available to institutional subscribers at Science Direct.

The take-home message is don't obsess over it. We have the tools to jump over the gorilla.


The fall of high-frequency trading: A survey of competition and profits

Jean-Philippe Serberaa,b,∗, Pascal PaumardcaBEAR Lab, Université Internationale de Rabat, Rabat Business School, Technopolis – Rocade de Rabat-Salé,11100 Salé El Jadida, MoroccobInstitut Africain de Risk Management, 35 Rue de la Bienfaisance, 75008 Paris, FrancecLaboratoire IRMAPE, Groupe ESC PAU, Campus Universitaire, 3 Rue Saint-John Perse, 64000 Pau, France



a b s t r a c t

We investigate high-frequency trading (HFT) strategies, inventorying the strategies already studied in the literature and introducing innovative strategies detected by private institutional research. To this end, we expand the existing classification, and we offer names for new categories. In a complementary but original manner, we introduce counter reactions from professional traders in response to HFT predatory strategies. These human answers reverse the usual framework of competition between high-frequency traders (HFTs) and low frequency traders (LFTs) and also widen this cadre to HFTs algos (predators) versus execution algos. This survey notes that a continuous increase in competition, between high-speed trading algorithms themselves through predatory strategies and from professional human traders adapting and building adequate responses has made the business more difficult and has led to shrinking profits for HFT. In the end, we believe that excessive competition and a change in the current regulation (favorable to HFT) could kill the goose that laid the golden egg.
© 2015 Elsevier B.V. All rights reserved.


Introduction

Since 2009, when a profit peak was reached, we have witnessed a decline in earnings, volume traded and market shares for the first time in the history of HFT. HFT strategies, which are implemented by complex algorithms to analyze multiple markets and execute orders based on market conditions, are widely studied in the finance literature. Smith (2010), Hoffmann(2014), and Menkveld (2014) examine the impact of HFT strategies on market quality and microstructure. Hendershott andRiordan (2013), Jarnecic and Snape (2014), and Harris (2013) focus more precisely on liquidity, while Boehmer and Wu (2013) investigate on price discovery. Also, Biais and Woolley (2012) modelize asymmetric information problems, and Angel and McCabe (2013) are concerned with the fairness of financial markets. In a complementary manner, authors like Smales (2014) or Kollias et al. (2013) probe HFT’s reactions to exogenous news or shocks such as the London bombings, and analyze theirimpact on market quality. In a more technical way, Brandaouy et al. (2014) survey the impact of (Kolmogorov) algos’ design on price dynamics.

Conclusions

In the following section, we will first sum up our findings as detailed in the whole article, then highlight some limitations and constraints that have truly hampered our research, but which could open up venues for further research and provide better insight into HFT competition and profits. Frequently, HFT has been considered to be a true cash-machine only capped by the boundaries of physics (Kearns et al.,2010), such as the speed of light or the available technology, with profits growing along with the technical progress. The description of competition mechanisms between HFTs and LFTs made by O’Hara (2014) and how the former overcomes the latter to profit from them are related questions in the literature. Although the competition framework already exists, its scope is limited to HFTs versus LFTs, and its understanding remains partial. The reversed competition, which has been largely ignored, where LFTs potentially harm HFTs, constitutes the most important gap. Different types of competition between execution algos and ultra-high speed predatory algos have been partially categorized by Aldridge (2009). However, despite a growing interest in predatory strategies, there seems to have been little investigation into their functioning and performance. In this survey, we tried to gather evidence of a decline in profits.




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